Overview

Dataset statistics

Number of variables25
Number of observations40428
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.5 MiB
Average record size in memory713.0 B

Variable types

CAT12
NUM12
BOOL1

Warnings

site_id has a high cardinality: 1070 distinct values High cardinality
site_domain has a high cardinality: 914 distinct values High cardinality
app_id has a high cardinality: 885 distinct values High cardinality
app_domain has a high cardinality: 60 distinct values High cardinality
device_id has a high cardinality: 6766 distinct values High cardinality
device_ip has a high cardinality: 33971 distinct values High cardinality
device_model has a high cardinality: 2388 distinct values High cardinality
C17 is highly correlated with C14High correlation
C14 is highly correlated with C17High correlation
id has unique values Unique
banner_pos has 28944 (71.6%) zeros Zeros

Reproduction

Analysis started2020-12-15 12:41:51.597564
Analysis finished2020-12-15 12:42:13.161439
Duration21.56 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

Distinct38514
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean204539.028
Minimum3
Maximum411432
Zeros0
Zeros (%)0.0%
Memory size316.0 KiB
2020-12-15T12:42:13.257701image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile20464.9
Q1102220.75
median204238.5
Q3307653.75
95-th percentile390020.4
Maximum411432
Range411429
Interquartile range (IQR)205433

Descriptive statistics

Standard deviation118658.9884
Coefficient of variation (CV)0.5801288367
Kurtosis-1.203124498
Mean204539.028
Median Absolute Deviation (MAD)102753
Skewness0.009393202936
Sum8269103825
Variance1.407995552e+10
MonotocityNot monotonic
2020-12-15T12:42:13.390526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
889474< 0.1%
 
3394264< 0.1%
 
2290764< 0.1%
 
3682833< 0.1%
 
3102053< 0.1%
 
1290183< 0.1%
 
2686563< 0.1%
 
2340533< 0.1%
 
2726073< 0.1%
 
1799913< 0.1%
 
2673233< 0.1%
 
3362603< 0.1%
 
3927823< 0.1%
 
934003< 0.1%
 
2152603< 0.1%
 
3596423< 0.1%
 
3872283< 0.1%
 
56903< 0.1%
 
100283< 0.1%
 
1503383< 0.1%
 
933413< 0.1%
 
3758703< 0.1%
 
3875743< 0.1%
 
796863< 0.1%
 
2901473< 0.1%
 
Other values (38489)4035099.8%
 
ValueCountFrequency (%) 
31< 0.1%
 
71< 0.1%
 
131< 0.1%
 
141< 0.1%
 
201< 0.1%
 
291< 0.1%
 
491< 0.1%
 
511< 0.1%
 
591< 0.1%
 
651< 0.1%
 
ValueCountFrequency (%) 
4114321< 0.1%
 
4110451< 0.1%
 
4110421< 0.1%
 
4109741< 0.1%
 
4109441< 0.1%
 
4109261< 0.1%
 
4108191< 0.1%
 
4107781< 0.1%
 
4105621< 0.1%
 
4104201< 0.1%
 

id
Real number (ℝ≥0)

UNIQUE

Distinct40428
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.187285826e+18
Minimum7.191322752e+13
Maximum1.844668479e+19
Zeros0
Zeros (%)0.0%
Memory size316.0 KiB
2020-12-15T12:42:13.533962image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7.191322752e+13
5-th percentile8.819140816e+17
Q14.544679843e+18
median9.193564096e+18
Q31.380762142e+19
95-th percentile1.753153336e+19
Maximum1.844668479e+19
Range1.844661288e+19
Interquartile range (IQR)9.26294158e+18

Descriptive statistics

Standard deviation5.333998435e+18
Coefficient of variation (CV)0.5805847925
Kurtosis-1.202868495
Mean9.187285826e+18
Median Absolute Deviation (MAD)4.62891684e+18
Skewness0.003900568779
Sum3.714235914e+23
Variance2.845153931e+37
MonotocityNot monotonic
2020-12-15T12:42:13.664496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.701291688e+191< 0.1%
 
2.217218676e+181< 0.1%
 
6.385943915e+181< 0.1%
 
1.593358684e+191< 0.1%
 
1.827104205e+191< 0.1%
 
1.40304105e+191< 0.1%
 
7.302404434e+181< 0.1%
 
1.68810531e+191< 0.1%
 
2.835425174e+181< 0.1%
 
9.401729309e+181< 0.1%
 
1.669419904e+191< 0.1%
 
1.507922553e+191< 0.1%
 
1.658695978e+191< 0.1%
 
7.858272689e+181< 0.1%
 
1.268531715e+191< 0.1%
 
8.415744407e+181< 0.1%
 
9.807579274e+181< 0.1%
 
9.37681283e+181< 0.1%
 
4.584576575e+181< 0.1%
 
1.713488511e+191< 0.1%
 
1.819853702e+191< 0.1%
 
8.324554674e+181< 0.1%
 
9.13478563e+181< 0.1%
 
1.404348179e+191< 0.1%
 
1.090212728e+191< 0.1%
 
Other values (40403)4040399.9%
 
ValueCountFrequency (%) 
7.191322752e+131< 0.1%
 
1.670657308e+151< 0.1%
 
1.710068637e+151< 0.1%
 
2.772472707e+151< 0.1%
 
3.488996135e+151< 0.1%
 
3.671448166e+151< 0.1%
 
4.594020177e+151< 0.1%
 
6.887657402e+151< 0.1%
 
6.928277311e+151< 0.1%
 
7.136301019e+151< 0.1%
 
ValueCountFrequency (%) 
1.844668479e+191< 0.1%
 
1.844629455e+191< 0.1%
 
1.844623826e+191< 0.1%
 
1.844527283e+191< 0.1%
 
1.844436587e+191< 0.1%
 
1.844427125e+191< 0.1%
 
1.844413929e+191< 0.1%
 
1.844394262e+191< 0.1%
 
1.844326526e+191< 0.1%
 
1.844272606e+191< 0.1%
 

click
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
0
33436 
1
6992 
ValueCountFrequency (%) 
03343682.7%
 
1699217.3%
 
2020-12-15T12:42:13.760283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

hour
Real number (ℝ≥0)

Distinct240
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14102558.26
Minimum14102100
Maximum14103023
Zeros0
Zeros (%)0.0%
Memory size316.0 KiB
2020-12-15T12:42:13.839688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum14102100
5-th percentile14102109
Q114102304
median14102602
Q314102814
95-th percentile14103012
Maximum14103023
Range923
Interquartile range (IQR)510

Descriptive statistics

Standard deviation296.6779145
Coefficient of variation (CV)2.103716992e-05
Kurtosis-1.336302522
Mean14102558.26
Median Absolute Deviation (MAD)287
Skewness-0.004908702167
Sum5.701382253e+11
Variance88017.78494
MonotocityNot monotonic
2020-12-15T12:42:13.963588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
141022094651.2%
 
141028134331.1%
 
141022104291.1%
 
141022124051.0%
 
141022113931.0%
 
141028143800.9%
 
141030043410.8%
 
141022083250.8%
 
141022133230.8%
 
141028093220.8%
 
141028082950.7%
 
141022052930.7%
 
141022062900.7%
 
141028162890.7%
 
141028152850.7%
 
141028122830.7%
 
141028172780.7%
 
141023042760.7%
 
141028102670.7%
 
141030142660.7%
 
141024172630.7%
 
141021052630.7%
 
141021042620.6%
 
141030132550.6%
 
141023152550.6%
 
Other values (215)3249280.4%
 
ValueCountFrequency (%) 
141021001180.3%
 
141021011440.4%
 
141021022100.5%
 
141021031880.5%
 
141021042620.6%
 
141021052630.7%
 
141021062450.6%
 
141021072140.5%
 
141021082160.5%
 
141021092260.6%
 
ValueCountFrequency (%) 
14103023810.2%
 
14103022990.2%
 
141030211280.3%
 
141030201190.3%
 
141030191260.3%
 
141030181530.4%
 
141030171550.4%
 
141030162250.6%
 
141030152280.6%
 
141030142660.7%
 

C1
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1004.965173
Minimum1001
Maximum1012
Zeros0
Zeros (%)0.0%
Memory size316.0 KiB
2020-12-15T12:42:14.067578image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1002
Q11005
median1005
Q31005
95-th percentile1005
Maximum1012
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.083234994
Coefficient of variation (CV)0.001077883119
Kurtosis15.31986967
Mean1004.965173
Median Absolute Deviation (MAD)0
Skewness1.833024901
Sum40628732
Variance1.173398053
MonotocityNot monotonic
2020-12-15T12:42:14.168368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
10053721092.0%
 
100221955.4%
 
10108642.1%
 
10121190.3%
 
1007260.1%
 
100110< 0.1%
 
10084< 0.1%
 
ValueCountFrequency (%) 
100110< 0.1%
 
100221955.4%
 
10053721092.0%
 
1007260.1%
 
10084< 0.1%
 
10108642.1%
 
10121190.3%
 
ValueCountFrequency (%) 
10121190.3%
 
10108642.1%
 
10084< 0.1%
 
1007260.1%
 
10053721092.0%
 
100221955.4%
 
100110< 0.1%
 

banner_pos
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2925942416
Minimum0
Maximum7
Zeros28944
Zeros (%)71.6%
Memory size316.0 KiB
2020-12-15T12:42:14.266522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.512632676
Coefficient of variation (CV)1.752025854
Kurtosis34.90704295
Mean0.2925942416
Median Absolute Deviation (MAD)0
Skewness3.485348346
Sum11829
Variance0.2627922605
MonotocityNot monotonic
2020-12-15T12:42:14.361451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
02894471.6%
 
11141228.2%
 
7490.1%
 
211< 0.1%
 
48< 0.1%
 
54< 0.1%
 
ValueCountFrequency (%) 
02894471.6%
 
11141228.2%
 
211< 0.1%
 
48< 0.1%
 
54< 0.1%
 
7490.1%
 
ValueCountFrequency (%) 
7490.1%
 
54< 0.1%
 
48< 0.1%
 
211< 0.1%
 
11141228.2%
 
02894471.6%
 

site_id
Categorical

HIGH CARDINALITY

Distinct1070
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
85f751fd
14398 
1fbe01fe
6534 
e151e245
2777 
d9750ee7
 
1001
5b08c53b
 
926
Other values (1065)
14792 
ValueCountFrequency (%) 
85f751fd1439835.6%
 
1fbe01fe653416.2%
 
e151e24527776.9%
 
d9750ee710012.5%
 
5b08c53b9262.3%
 
856e6d3f7611.9%
 
5b4d2eda7471.8%
 
a78530074421.1%
 
5ee41ff23670.9%
 
b7e9786d3510.9%
 
6399eda63410.8%
 
5bcf81a23390.8%
 
6256f5b43360.8%
 
17caea143070.8%
 
57ef2c873030.7%
 
83a0ad1a2850.7%
 
57fe1b202810.7%
 
e8f79e602800.7%
 
0a7429142760.7%
 
e4d8dd7b2650.7%
 
d61379152400.6%
 
6c5b482c1940.5%
 
5114c6721770.4%
 
12fb41211750.4%
 
9a9775311710.4%
 
Other values (1045)815420.2%
 
2020-12-15T12:42:14.507385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique432 ?
Unique (%)1.1%
2020-12-15T12:42:14.639333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
f4911115.2%
 
54578614.2%
 
14048612.5%
 
e304609.4%
 
7251297.8%
 
d232667.2%
 
8220076.8%
 
b154364.8%
 
0144314.5%
 
4103933.2%
 
299773.1%
 
a83122.6%
 
677452.4%
 
971172.2%
 
369672.2%
 
c68012.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19003858.8%
 
Lowercase Letter13338641.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
54578624.1%
 
14048621.3%
 
72512913.2%
 
82200711.6%
 
0144317.6%
 
4103935.5%
 
299775.3%
 
677454.1%
 
971173.7%
 
369673.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
f4911136.8%
 
e3046022.8%
 
d2326617.4%
 
b1543611.6%
 
a83126.2%
 
c68015.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19003858.8%
 
Latin13338641.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
54578624.1%
 
14048621.3%
 
72512913.2%
 
82200711.6%
 
0144317.6%
 
4103935.5%
 
299775.3%
 
677454.1%
 
971173.7%
 
369673.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
f4911136.8%
 
e3046022.8%
 
d2326617.4%
 
b1543611.6%
 
a83126.2%
 
c68015.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII323424100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
f4911115.2%
 
54578614.2%
 
14048612.5%
 
e304609.4%
 
7251297.8%
 
d232667.2%
 
8220076.8%
 
b154364.8%
 
0144314.5%
 
4103933.2%
 
299773.1%
 
a83122.6%
 
677452.4%
 
971172.2%
 
369672.2%
 
c68012.1%
 

site_domain
Categorical

HIGH CARDINALITY

Distinct914
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
c4e18dd6
14981 
f3845767
6534 
7e091613
3460 
7687a86e
 
1279
98572c79
 
1036
Other values (909)
13138 
ValueCountFrequency (%) 
c4e18dd61498137.1%
 
f3845767653416.2%
 
7e09161334608.6%
 
7687a86e12793.2%
 
98572c7910362.6%
 
16a36ef38232.0%
 
58a89a437611.9%
 
17d996e63730.9%
 
9d54950b3650.9%
 
b12b9f853560.9%
 
968765cd3410.8%
 
28f930293360.8%
 
0dde25ec3070.8%
 
bd6d812f3030.7%
 
5c9ae8672850.7%
 
d262cf1e2840.7%
 
5b6265962820.7%
 
c43427842800.7%
 
510bd8392760.7%
 
a17bde682650.7%
 
bb1ef3342400.6%
 
6b59f0792260.6%
 
7256c6232090.5%
 
a434fa422040.5%
 
3f2f38191770.4%
 
Other values (889)644515.9%
 
2020-12-15T12:42:14.772862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique410 ?
Unique (%)1.0%
2020-12-15T12:42:14.895012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
d3615011.2%
 
63598711.1%
 
83271010.1%
 
1280708.7%
 
4273818.5%
 
7262408.1%
 
e255797.9%
 
c215506.7%
 
3178135.5%
 
5148104.6%
 
9140334.3%
 
f126093.9%
 
281892.5%
 
079412.5%
 
a75762.3%
 
b67862.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number21317465.9%
 
Lowercase Letter11025034.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
63598716.9%
 
83271015.3%
 
12807013.2%
 
42738112.8%
 
72624012.3%
 
3178138.4%
 
5148106.9%
 
9140336.6%
 
281893.8%
 
079413.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
d3615032.8%
 
e2557923.2%
 
c2155019.5%
 
f1260911.4%
 
a75766.9%
 
b67866.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common21317465.9%
 
Latin11025034.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
63598716.9%
 
83271015.3%
 
12807013.2%
 
42738112.8%
 
72624012.3%
 
3178138.4%
 
5148106.9%
 
9140336.6%
 
281893.8%
 
079413.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
d3615032.8%
 
e2557923.2%
 
c2155019.5%
 
f1260911.4%
 
a75766.9%
 
b67866.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII323424100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
d3615011.2%
 
63598711.1%
 
83271010.1%
 
1280708.7%
 
4273818.5%
 
7262408.1%
 
e255797.9%
 
c215506.7%
 
3178135.5%
 
5148104.6%
 
9140334.3%
 
f126093.9%
 
281892.5%
 
079412.5%
 
a75762.3%
 
b67862.1%
 

site_category
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
50e219e0
16331 
f028772b
12914 
28905ebd
7375 
3e814130
3037 
f66779e6
 
230
Other values (13)
 
541
ValueCountFrequency (%) 
50e219e01633140.4%
 
f028772b1291431.9%
 
28905ebd737518.2%
 
3e81413030377.5%
 
f66779e62300.6%
 
75fa27f61510.4%
 
335d28a81490.4%
 
76b2941d1030.3%
 
c0dd3be3390.1%
 
dedf689d300.1%
 
72722551270.1%
 
70fb0e2915< 0.1%
 
0569f92813< 0.1%
 
8fd0aea47< 0.1%
 
42a36e143< 0.1%
 
bcf865d92< 0.1%
 
9ccfa2ea1< 0.1%
 
a818d37a1< 0.1%
 
2020-12-15T12:42:15.005930image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-12-15T12:42:15.111217image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05607717.3%
 
25005015.5%
 
e4339913.4%
 
7267638.3%
 
9241137.5%
 
5240757.4%
 
8236787.3%
 
1225397.0%
 
b204486.3%
 
f135144.2%
 
d78052.4%
 
364542.0%
 
431531.0%
 
69920.3%
 
a3210.1%
 
c43< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number23789473.6%
 
Lowercase Letter8553026.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e4339950.7%
 
b2044823.9%
 
f1351415.8%
 
d78059.1%
 
a3210.4%
 
c430.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05607723.6%
 
25005021.0%
 
72676311.2%
 
92411310.1%
 
52407510.1%
 
82367810.0%
 
1225399.5%
 
364542.7%
 
431531.3%
 
69920.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common23789473.6%
 
Latin8553026.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e4339950.7%
 
b2044823.9%
 
f1351415.8%
 
d78059.1%
 
a3210.4%
 
c430.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
05607723.6%
 
25005021.0%
 
72676311.2%
 
92411310.1%
 
52407510.1%
 
82367810.0%
 
1225399.5%
 
364542.7%
 
431531.3%
 
69920.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII323424100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05607717.3%
 
25005015.5%
 
e4339913.4%
 
7267638.3%
 
9241137.5%
 
5240757.4%
 
8236787.3%
 
1225397.0%
 
b204486.3%
 
f135144.2%
 
d78052.4%
 
364542.0%
 
431531.0%
 
69920.3%
 
a3210.1%
 
c43< 0.1%
 

app_id
Categorical

HIGH CARDINALITY

Distinct885
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
ecad2386
26030 
92f5800b
 
1597
e2fcccd2
 
1124
9c13b419
 
741
febd1138
 
733
Other values (880)
10203 
ValueCountFrequency (%) 
ecad23862603064.4%
 
92f5800b15974.0%
 
e2fcccd211242.8%
 
9c13b4197411.8%
 
febd11387331.8%
 
7358e05e5871.5%
 
a5184c224731.2%
 
d36838b14501.1%
 
54c5d5454351.1%
 
685d1c4c4081.0%
 
03528b273320.8%
 
f0d41ff12740.7%
 
e97398282720.7%
 
66f5e02e2600.6%
 
e2a1ca372560.6%
 
51cedd4e2300.6%
 
98fed7912270.6%
 
03a08c3f2110.5%
 
732063971940.5%
 
f53417e11870.5%
 
e96773f01610.4%
 
ce183bbd1290.3%
 
396df8011210.3%
 
be7c618d1200.3%
 
1dc72b4d1070.3%
 
Other values (860)476911.8%
 
2020-12-15T12:42:15.230404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique408 ?
Unique (%)1.0%
2020-12-15T12:42:15.354389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
c3533910.9%
 
83496810.8%
 
23461610.7%
 
e3368310.4%
 
33366010.4%
 
d3313910.2%
 
6302019.3%
 
a298199.2%
 
f85962.7%
 
585392.6%
 
179362.5%
 
077362.4%
 
969942.2%
 
b67002.1%
 
461891.9%
 
753091.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number17614854.5%
 
Lowercase Letter14727645.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
c3533924.0%
 
e3368322.9%
 
d3313922.5%
 
a2981920.2%
 
f85965.8%
 
b67004.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
83496819.9%
 
23461619.7%
 
33366019.1%
 
63020117.1%
 
585394.8%
 
179364.5%
 
077364.4%
 
969944.0%
 
461893.5%
 
753093.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common17614854.5%
 
Latin14727645.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
c3533924.0%
 
e3368322.9%
 
d3313922.5%
 
a2981920.2%
 
f85965.8%
 
b67004.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
83496819.9%
 
23461619.7%
 
33366019.1%
 
63020117.1%
 
585394.8%
 
179364.5%
 
077364.4%
 
969944.0%
 
461893.5%
 
753093.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII323424100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
c3533910.9%
 
83496810.8%
 
23461610.7%
 
e3368310.4%
 
33366010.4%
 
d3313910.2%
 
6302019.3%
 
a298199.2%
 
f85962.7%
 
585392.6%
 
179362.5%
 
077362.4%
 
969942.2%
 
b67002.1%
 
461891.9%
 
753091.6%
 

app_domain
Categorical

HIGH CARDINALITY

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
7801e8d9
27371 
2347f47a
5223 
ae637522
 
1897
5c5a694b
 
1124
d9b5648e
 
740
Other values (55)
4073 
ValueCountFrequency (%) 
7801e8d92737167.7%
 
2347f47a522312.9%
 
ae63752218974.7%
 
5c5a694b11242.8%
 
d9b5648e7401.8%
 
82e279967331.8%
 
0e8616ad6401.6%
 
b9528b136171.5%
 
b8d325c35171.3%
 
aefc06bd2850.7%
 
df32afa92760.7%
 
6f7ca2ba2600.6%
 
33da2e742260.6%
 
5b9c592b1050.3%
 
885c7f3f650.2%
 
45a51db4580.1%
 
b5f3b24a550.1%
 
5c620f04500.1%
 
813f3323260.1%
 
0654b444260.1%
 
ad63ec9b15< 0.1%
 
a8b0bf2013< 0.1%
 
c6824def12< 0.1%
 
99b4c80610< 0.1%
 
448ca2e39< 0.1%
 
Other values (35)750.2%
 
2020-12-15T12:42:15.485487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique14 ?
Unique (%)< 0.1%
2020-12-15T12:42:15.600997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
85821818.0%
 
74105112.7%
 
e319799.9%
 
9318689.9%
 
d301809.3%
 
1287538.9%
 
0284878.8%
 
4129464.0%
 
2126823.9%
 
a106573.3%
 
397893.0%
 
f66712.1%
 
565702.0%
 
664542.0%
 
b46401.4%
 
c24790.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number23681873.2%
 
Lowercase Letter8660626.8%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
85821824.6%
 
74105117.3%
 
93186813.5%
 
12875312.1%
 
02848712.0%
 
4129465.5%
 
2126825.4%
 
397894.1%
 
565702.8%
 
664542.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e3197936.9%
 
d3018034.8%
 
a1065712.3%
 
f66717.7%
 
b46405.4%
 
c24792.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common23681873.2%
 
Latin8660626.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
85821824.6%
 
74105117.3%
 
93186813.5%
 
12875312.1%
 
02848712.0%
 
4129465.5%
 
2126825.4%
 
397894.1%
 
565702.8%
 
664542.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e3197936.9%
 
d3018034.8%
 
a1065712.3%
 
f66717.7%
 
b46405.4%
 
c24792.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII323424100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
85821818.0%
 
74105112.7%
 
e319799.9%
 
9318689.9%
 
d301809.3%
 
1287538.9%
 
0284878.8%
 
4129464.0%
 
2126823.9%
 
a106573.3%
 
397893.0%
 
f66712.1%
 
565702.0%
 
664542.0%
 
b46401.4%
 
c24790.8%
 

app_category
Categorical

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
07d7df22
26339 
0f2161f8
9571 
cef3e649
 
1643
8ded1f7a
 
1439
f95efa07
 
1120
Other values (16)
 
316
ValueCountFrequency (%) 
07d7df222633965.2%
 
0f2161f8957123.7%
 
cef3e64916434.1%
 
8ded1f7a14393.6%
 
f95efa0711202.8%
 
d1327cf51140.3%
 
09481d60450.1%
 
dc97ec06450.1%
 
75d80bbe400.1%
 
4ce2e9fc16< 0.1%
 
fc6fa53d14< 0.1%
 
a3c426889< 0.1%
 
0f9a328c9< 0.1%
 
4681bb9d6< 0.1%
 
879c24eb5< 0.1%
 
8df2e8424< 0.1%
 
2281a3403< 0.1%
 
a86a3e893< 0.1%
 
0bfbc3581< 0.1%
 
79f0b8601< 0.1%
 
a7fd01ec1< 0.1%
 
2020-12-15T12:42:15.710520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2020-12-15T12:42:15.821799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
26241619.3%
 
d5582517.3%
 
75544317.1%
 
f5097715.8%
 
03722111.5%
 
1207506.4%
 
6113373.5%
 
8111523.4%
 
e59751.8%
 
928930.9%
 
a26010.8%
 
c19180.6%
 
317960.6%
 
417310.5%
 
512890.4%
 
b100< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number20602863.7%
 
Lowercase Letter11739636.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
26241630.3%
 
75544326.9%
 
03722118.1%
 
12075010.1%
 
6113375.5%
 
8111525.4%
 
928931.4%
 
317960.9%
 
417310.8%
 
512890.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
d5582547.6%
 
f5097743.4%
 
e59755.1%
 
a26012.2%
 
c19181.6%
 
b1000.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common20602863.7%
 
Latin11739636.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
26241630.3%
 
75544326.9%
 
03722118.1%
 
12075010.1%
 
6113375.5%
 
8111525.4%
 
928931.4%
 
317960.9%
 
417310.8%
 
512890.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
d5582547.6%
 
f5097743.4%
 
e59755.1%
 
a26012.2%
 
c19181.6%
 
b1000.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII323424100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
26241619.3%
 
d5582517.3%
 
75544317.1%
 
f5097715.8%
 
03722111.5%
 
1207506.4%
 
6113373.5%
 
8111523.4%
 
e59751.8%
 
928930.9%
 
a26010.8%
 
c19180.6%
 
317960.6%
 
417310.5%
 
512890.4%
 
b100< 0.1%
 

device_id
Categorical

HIGH CARDINALITY

Distinct6766
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
a99f214a
33502 
c357dbff
 
27
0f7c61dc
 
21
936e92fb
 
15
afeffc18
 
7
Other values (6761)
6856 
ValueCountFrequency (%) 
a99f214a3350282.9%
 
c357dbff270.1%
 
0f7c61dc210.1%
 
936e92fb15< 0.1%
 
afeffc187< 0.1%
 
3cdb40527< 0.1%
 
987552d16< 0.1%
 
b09da1c45< 0.1%
 
cef4c8cc5< 0.1%
 
d857ffbb5< 0.1%
 
28dc86874< 0.1%
 
1f056b4e4< 0.1%
 
a41547ef3< 0.1%
 
bc3351453< 0.1%
 
89330ac73< 0.1%
 
5cad66ba3< 0.1%
 
ea04480e2< 0.1%
 
66bee6332< 0.1%
 
176908322< 0.1%
 
91ecfb012< 0.1%
 
c68b88db2< 0.1%
 
6131d6d42< 0.1%
 
ed83f03d2< 0.1%
 
7a7834d42< 0.1%
 
5c2b38862< 0.1%
 
Other values (6741)679016.8%
 
2020-12-15T12:42:15.973617image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6692 ?
Unique (%)16.6%
2020-12-15T12:42:16.316016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
97039121.8%
 
a7034421.7%
 
f3705411.5%
 
23695411.4%
 
13691411.4%
 
43690911.4%
 
835621.1%
 
335571.1%
 
e35471.1%
 
b35081.1%
 
635021.1%
 
034761.1%
 
c34731.1%
 
d34721.1%
 
534121.1%
 
733491.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number20202662.5%
 
Lowercase Letter12139837.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a7034457.9%
 
f3705430.5%
 
e35472.9%
 
b35082.9%
 
c34732.9%
 
d34722.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
97039134.8%
 
23695418.3%
 
13691418.3%
 
43690918.3%
 
835621.8%
 
335571.8%
 
635021.7%
 
034761.7%
 
534121.7%
 
733491.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Common20202662.5%
 
Latin12139837.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a7034457.9%
 
f3705430.5%
 
e35472.9%
 
b35082.9%
 
c34732.9%
 
d34722.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
97039134.8%
 
23695418.3%
 
13691418.3%
 
43690918.3%
 
835621.8%
 
335571.8%
 
635021.7%
 
034761.7%
 
534121.7%
 
733491.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII323424100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
97039121.8%
 
a7034421.7%
 
f3705411.5%
 
23695411.4%
 
13691411.4%
 
43690911.4%
 
835621.1%
 
335571.1%
 
e35471.1%
 
b35081.1%
 
635021.1%
 
034761.1%
 
c34731.1%
 
d34721.1%
 
534121.1%
 
733491.0%
 

device_ip
Categorical

HIGH CARDINALITY

Distinct33971
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
6b9769f2
 
216
431b3174
 
133
930ec31d
 
90
af9205f9
 
90
af62faf4
 
88
Other values (33966)
39811 
ValueCountFrequency (%) 
6b9769f22160.5%
 
431b31741330.3%
 
930ec31d900.2%
 
af9205f9900.2%
 
af62faf4880.2%
 
009a7861850.2%
 
0489ce3f850.2%
 
285aa37d830.2%
 
2f323f36810.2%
 
ddd2926e800.2%
 
c6563308790.2%
 
6394f6f6790.2%
 
8a014cbb790.2%
 
d90a7774720.2%
 
9b1fe278660.2%
 
1cf29716650.2%
 
75bb1b58630.2%
 
488a9a3e630.2%
 
57cd4006630.2%
 
a8536f3a620.2%
 
ceffea69520.1%
 
07875ea4390.1%
 
693bff3e360.1%
 
b0070d9a340.1%
 
ff1c4f79330.1%
 
Other values (33946)3851295.3%
 
2020-12-15T12:42:16.507371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique31568 ?
Unique (%)78.1%
2020-12-15T12:42:16.636913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
f210266.5%
 
9209706.5%
 
6207766.4%
 
a205676.4%
 
7204806.3%
 
3204646.3%
 
b201926.2%
 
1201606.2%
 
2200716.2%
 
0200626.2%
 
4199886.2%
 
5199596.2%
 
e198436.1%
 
d196496.1%
 
8196386.1%
 
c195796.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number20256862.6%
 
Lowercase Letter12085637.4%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
92097010.4%
 
62077610.3%
 
72048010.1%
 
32046410.1%
 
12016010.0%
 
2200719.9%
 
0200629.9%
 
4199889.9%
 
5199599.9%
 
8196389.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
f2102617.4%
 
a2056717.0%
 
b2019216.7%
 
e1984316.4%
 
d1964916.3%
 
c1957916.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common20256862.6%
 
Latin12085637.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
92097010.4%
 
62077610.3%
 
72048010.1%
 
32046410.1%
 
12016010.0%
 
2200719.9%
 
0200629.9%
 
4199889.9%
 
5199599.9%
 
8196389.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
f2102617.4%
 
a2056717.0%
 
b2019216.7%
 
e1984316.4%
 
d1964916.3%
 
c1957916.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII323424100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
f210266.5%
 
9209706.5%
 
6207766.4%
 
a205676.4%
 
7204806.3%
 
3204646.3%
 
b201926.2%
 
1201606.2%
 
2200716.2%
 
0200626.2%
 
4199886.2%
 
5199596.2%
 
e198436.1%
 
d196496.1%
 
8196386.1%
 
c195796.1%
 

device_model
Categorical

HIGH CARDINALITY

Distinct2388
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
8a4875bd
 
2425
d787e91b
 
1484
1f0bc64f
 
1445
76dc4769
 
802
be6db1d7
 
732
Other values (2383)
33540 
ValueCountFrequency (%) 
8a4875bd24256.0%
 
d787e91b14843.7%
 
1f0bc64f14453.6%
 
76dc47698022.0%
 
be6db1d77321.8%
 
4ea23a136701.7%
 
a0f5f8796371.6%
 
7abbbd5c5891.5%
 
ecb851b25851.4%
 
d4897fef5101.3%
 
5096d1344781.2%
 
e1eae7154361.1%
 
711ee1204351.1%
 
1ccc78354021.0%
 
c6263d8a3991.0%
 
84ebbcd43961.0%
 
be74e6fe3800.9%
 
fce665243670.9%
 
981edffc3640.9%
 
f07e20f83630.9%
 
0bcabeaf3570.9%
 
3bb1ddd73570.9%
 
3bd9e8e73410.8%
 
0eb711ec3390.8%
 
779d90c23110.8%
 
Other values (2363)2482461.4%
 
2020-12-15T12:42:16.773492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique838 ?
Unique (%)2.1%
2020-12-15T12:42:16.886833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
b258038.0%
 
7242737.5%
 
e238077.4%
 
d228747.1%
 
1221136.8%
 
8220306.8%
 
4215386.7%
 
a198576.1%
 
f197316.1%
 
6192916.0%
 
5185915.7%
 
c184375.7%
 
9175935.4%
 
0161635.0%
 
3157404.9%
 
2155834.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number19291559.6%
 
Lowercase Letter13050940.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
b2580319.8%
 
e2380718.2%
 
d2287417.5%
 
a1985715.2%
 
f1973115.1%
 
c1843714.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
72427312.6%
 
12211311.5%
 
82203011.4%
 
42153811.2%
 
61929110.0%
 
5185919.6%
 
9175939.1%
 
0161638.4%
 
3157408.2%
 
2155838.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common19291559.6%
 
Latin13050940.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
b2580319.8%
 
e2380718.2%
 
d2287417.5%
 
a1985715.2%
 
f1973115.1%
 
c1843714.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
72427312.6%
 
12211311.5%
 
82203011.4%
 
42153811.2%
 
61929110.0%
 
5185919.6%
 
9175939.1%
 
0161638.4%
 
3157408.2%
 
2155838.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII323424100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
b258038.0%
 
7242737.5%
 
e238077.4%
 
d228747.1%
 
1221136.8%
 
8220306.8%
 
4215386.7%
 
a198576.1%
 
f197316.1%
 
6192916.0%
 
5185915.7%
 
c184375.7%
 
9175935.4%
 
0161635.0%
 
3157404.9%
 
2155834.8%
 

device_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
1
37369 
0
 
2195
4
 
734
5
 
130
ValueCountFrequency (%) 
13736992.4%
 
021955.4%
 
47341.8%
 
51300.3%
 
2020-12-15T12:42:16.991699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-15T12:42:17.063826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T12:42:17.144878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
13736992.4%
 
021955.4%
 
47341.8%
 
51300.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number40428100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
13736992.4%
 
021955.4%
 
47341.8%
 
51300.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common40428100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
13736992.4%
 
021955.4%
 
47341.8%
 
51300.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII40428100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
13736992.4%
 
021955.4%
 
47341.8%
 
51300.3%
 

device_conn_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
0
34876 
2
 
3305
3
 
2210
5
 
37
ValueCountFrequency (%) 
03487686.3%
 
233058.2%
 
322105.5%
 
5370.1%
 
2020-12-15T12:42:17.249222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-15T12:42:17.323847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T12:42:17.406339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
03487686.3%
 
233058.2%
 
322105.5%
 
5370.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number40428100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
03487686.3%
 
233058.2%
 
322105.5%
 
5370.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common40428100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
03487686.3%
 
233058.2%
 
322105.5%
 
5370.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII40428100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
03487686.3%
 
233058.2%
 
322105.5%
 
5370.1%
 

C14
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1420
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18839.94657
Minimum375
Maximum24041
Zeros0
Zeros (%)0.0%
Memory size316.0 KiB
2020-12-15T12:42:17.518826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum375
5-th percentile6393
Q116920
median20345
Q321893
95-th percentile23561
Maximum24041
Range23666
Interquartile range (IQR)4973

Descriptive statistics

Standard deviation4930.071862
Coefficient of variation (CV)0.2616818388
Kurtosis3.501245186
Mean18839.94657
Median Absolute Deviation (MAD)2337
Skewness-1.885516891
Sum761661360
Variance24305608.57
MonotocityNot monotonic
2020-12-15T12:42:17.646662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
46879452.3%
 
216118682.1%
 
211918012.0%
 
211897741.9%
 
197717521.9%
 
197727411.8%
 
162086801.7%
 
201086051.5%
 
157015641.4%
 
83305621.4%
 
157055521.4%
 
199505421.3%
 
157085141.3%
 
157075111.3%
 
157035111.3%
 
156994801.2%
 
157064761.2%
 
157044741.2%
 
157024551.1%
 
166154411.1%
 
238043971.0%
 
217683921.0%
 
217673640.9%
 
192513410.8%
 
226763330.8%
 
Other values (1395)2635365.2%
 
ValueCountFrequency (%) 
375820.2%
 
377880.2%
 
380660.2%
 
3812< 0.1%
 
452620.2%
 
454530.1%
 
456510.1%
 
463560.1%
 
78711< 0.1%
 
10371010.2%
 
ValueCountFrequency (%) 
240414< 0.1%
 
240408< 0.1%
 
24036300.1%
 
24035240.1%
 
24034650.2%
 
240332< 0.1%
 
240094< 0.1%
 
240082< 0.1%
 
240062< 0.1%
 
240053< 0.1%
 

C15
Real number (ℝ≥0)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean318.802711
Minimum120
Maximum1024
Zeros0
Zeros (%)0.0%
Memory size316.0 KiB
2020-12-15T12:42:17.760633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile300
Q1320
median320
Q3320
95-th percentile320
Maximum1024
Range904
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.19603533
Coefficient of variation (CV)0.06334963486
Kurtosis357.9848184
Mean318.802711
Median Absolute Deviation (MAD)0
Skewness14.88692545
Sum12888556
Variance407.8798432
MonotocityNot monotonic
2020-12-15T12:42:17.848129image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
3203775693.4%
 
30022995.7%
 
2162970.7%
 
728660.2%
 
1204< 0.1%
 
4803< 0.1%
 
10242< 0.1%
 
7681< 0.1%
 
ValueCountFrequency (%) 
1204< 0.1%
 
2162970.7%
 
30022995.7%
 
3203775693.4%
 
4803< 0.1%
 
728660.2%
 
7681< 0.1%
 
10242< 0.1%
 
ValueCountFrequency (%) 
10242< 0.1%
 
7681< 0.1%
 
728660.2%
 
4803< 0.1%
 
3203775693.4%
 
30022995.7%
 
2162970.7%
 
1204< 0.1%
 

C16
Real number (ℝ≥0)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.88527753
Minimum20
Maximum1024
Zeros0
Zeros (%)0.0%
Memory size316.0 KiB
2020-12-15T12:42:17.938802image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile50
Q150
median50
Q350
95-th percentile50
Maximum1024
Range1004
Interquartile range (IQR)0

Descriptive statistics

Standard deviation46.68628566
Coefficient of variation (CV)0.7795953795
Kurtosis32.78666811
Mean59.88527753
Median Absolute Deviation (MAD)0
Skewness5.190681762
Sum2421042
Variance2179.609269
MonotocityNot monotonic
2020-12-15T12:42:18.030529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
503818294.4%
 
25017714.4%
 
362970.7%
 
4801020.3%
 
90660.2%
 
204< 0.1%
 
3203< 0.1%
 
7682< 0.1%
 
10241< 0.1%
 
ValueCountFrequency (%) 
204< 0.1%
 
362970.7%
 
503818294.4%
 
90660.2%
 
25017714.4%
 
3203< 0.1%
 
4801020.3%
 
7682< 0.1%
 
10241< 0.1%
 
ValueCountFrequency (%) 
10241< 0.1%
 
7682< 0.1%
 
4801020.3%
 
3203< 0.1%
 
25017714.4%
 
90660.2%
 
503818294.4%
 
362970.7%
 
204< 0.1%
 

C17
Real number (ℝ≥0)

HIGH CORRELATION

Distinct381
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2111.703374
Minimum112
Maximum2756
Zeros0
Zeros (%)0.0%
Memory size316.0 KiB
2020-12-15T12:42:18.150055image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum112
5-th percentile547
Q11863
median2323
Q32526
95-th percentile2688
Maximum2756
Range2644
Interquartile range (IQR)663

Descriptive statistics

Standard deviation606.6805421
Coefficient of variation (CV)0.2872943944
Kurtosis2.269693267
Mean2111.703374
Median Absolute Deviation (MAD)301
Skewness-1.633235254
Sum85371944
Variance368061.2801
MonotocityNot monotonic
2020-12-15T12:42:18.290684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1722455411.3%
 
242415753.9%
 
222714983.7%
 
180012263.0%
 
4239452.3%
 
24808792.2%
 
25288282.0%
 
25028062.0%
 
25068032.0%
 
23747851.9%
 
18727651.9%
 
25457201.8%
 
19946191.5%
 
22996051.5%
 
18635681.4%
 
7615631.4%
 
25265541.4%
 
19935181.3%
 
23334961.2%
 
26654781.2%
 
18734681.2%
 
26764641.1%
 
25074231.0%
 
27263971.0%
 
26473820.9%
 
Other values (356)1850945.8%
 
ValueCountFrequency (%) 
1122380.6%
 
1222220.5%
 
15311< 0.1%
 
1782540.6%
 
1964< 0.1%
 
39420< 0.1%
 
4239452.3%
 
4792040.5%
 
5441000.2%
 
5471410.3%
 
ValueCountFrequency (%) 
275612< 0.1%
 
2755540.1%
 
2754650.2%
 
27532< 0.1%
 
2749320.1%
 
2748300.1%
 
2747930.2%
 
27453< 0.1%
 
27435< 0.1%
 
274218< 0.1%
 

C18
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size316.0 KiB
0
16670 
3
13784 
2
7197 
1
2777 
ValueCountFrequency (%) 
01667041.2%
 
31378434.1%
 
2719717.8%
 
127776.9%
 
2020-12-15T12:42:18.425362image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-15T12:42:18.508879image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T12:42:18.593120image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
01667041.2%
 
31378434.1%
 
2719717.8%
 
127776.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number40428100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01667041.2%
 
31378434.1%
 
2719717.8%
 
127776.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common40428100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
01667041.2%
 
31378434.1%
 
2719717.8%
 
127776.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII40428100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
01667041.2%
 
31378434.1%
 
2719717.8%
 
127776.9%
 

C19
Real number (ℝ≥0)

Distinct63
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.4293806
Minimum33
Maximum1839
Zeros0
Zeros (%)0.0%
Memory size316.0 KiB
2020-12-15T12:42:18.705644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile35
Q135
median39
Q3171
95-th percentile1063
Maximum1839
Range1806
Interquartile range (IQR)136

Descriptive statistics

Standard deviation354.3736092
Coefficient of variation (CV)1.544586871
Kurtosis3.775976039
Mean229.4293806
Median Absolute Deviation (MAD)4
Skewness2.142571974
Sum9275371
Variance125580.6549
MonotocityNot monotonic
2020-12-15T12:42:18.829280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
351206029.8%
 
39888222.0%
 
16732368.0%
 
16116294.0%
 
4714463.6%
 
132711132.8%
 
2979622.4%
 
1638882.2%
 
1758222.0%
 
6797531.9%
 
9356941.7%
 
6875881.5%
 
10635571.4%
 
415161.3%
 
334491.1%
 
8033971.0%
 
4313921.0%
 
13193840.9%
 
3033490.9%
 
2993120.8%
 
4193090.8%
 
1712940.7%
 
1692820.7%
 
4272770.7%
 
342660.7%
 
Other values (38)25716.4%
 
ValueCountFrequency (%) 
334491.1%
 
342660.7%
 
351206029.8%
 
381960.5%
 
39888222.0%
 
415161.3%
 
432070.5%
 
452< 0.1%
 
4714463.6%
 
16116294.0%
 
ValueCountFrequency (%) 
183919< 0.1%
 
1835290.1%
 
1831250.1%
 
1711870.2%
 
15835< 0.1%
 
1575210.1%
 
14511420.4%
 
132711132.8%
 
13193840.9%
 
1315370.1%
 

C20
Real number (ℝ)

Distinct148
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53450.75386
Minimum-1
Maximum100248
Zeros0
Zeros (%)0.0%
Memory size316.0 KiB
2020-12-15T12:42:18.956886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median100052
Q3100094
95-th percentile100191
Maximum100248
Range100249
Interquartile range (IQR)100095

Descriptive statistics

Standard deviation49942.05967
Coefficient of variation (CV)0.9343565069
Kurtosis-1.981609317
Mean53450.75386
Median Absolute Deviation (MAD)141
Skewness-0.1359650179
Sum2160907077
Variance2494209324
MonotocityNot monotonic
2020-12-15T12:42:19.087132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-11884346.6%
 
10008424156.0%
 
10014817584.3%
 
10011116694.1%
 
10007715953.9%
 
10007515743.9%
 
10008113753.4%
 
10008311252.8%
 
10015610242.5%
 
1001937411.8%
 
1001766271.6%
 
1000745731.4%
 
1000795541.4%
 
1001894911.2%
 
1000764671.2%
 
1001922490.6%
 
1001912360.6%
 
1001902270.6%
 
1000132240.6%
 
1001882070.5%
 
1000311700.4%
 
1001551570.4%
 
1001941470.4%
 
1001811390.3%
 
1001051370.3%
 
Other values (123)37049.2%
 
ValueCountFrequency (%) 
-11884346.6%
 
1000001360.3%
 
1000019< 0.1%
 
1000027< 0.1%
 
1000031090.3%
 
100004730.2%
 
100005730.2%
 
100012280.1%
 
1000132240.6%
 
1000161< 0.1%
 
ValueCountFrequency (%) 
100248210.1%
 
1002442< 0.1%
 
10024120< 0.1%
 
100233990.2%
 
1002294< 0.1%
 
100228660.2%
 
1002257< 0.1%
 
100221750.2%
 
100217460.1%
 
10021513< 0.1%
 

C21
Real number (ℝ≥0)

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.38458494
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Memory size316.0 KiB
2020-12-15T12:42:19.206080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q123
median61
Q395
95-th percentile221
Maximum255
Range254
Interquartile range (IQR)72

Descriptive statistics

Standard deviation69.75575247
Coefficient of variation (CV)0.8467087929
Kurtosis-0.177872427
Mean82.38458494
Median Absolute Deviation (MAD)38
Skewness1.122513532
Sum3330644
Variance4865.865003
MonotocityNot monotonic
2020-12-15T12:42:19.327426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
23912122.6%
 
221490712.1%
 
79464911.5%
 
4822115.5%
 
7121745.4%
 
6120465.1%
 
15718244.5%
 
3217334.3%
 
3315183.8%
 
5212173.0%
 
429732.4%
 
518472.1%
 
157541.9%
 
2126351.6%
 
435931.5%
 
2293971.0%
 
1173931.0%
 
133851.0%
 
163480.9%
 
1563450.9%
 
683270.8%
 
1592890.7%
 
952360.6%
 
462240.6%
 
2461990.5%
 
Other values (35)20835.2%
 
ValueCountFrequency (%) 
12< 0.1%
 
133851.0%
 
157541.9%
 
163480.9%
 
171580.4%
 
2011< 0.1%
 
23912122.6%
 
3217334.3%
 
3315183.8%
 
35520.1%
 
ValueCountFrequency (%) 
2553< 0.1%
 
253870.2%
 
25117< 0.1%
 
2461990.5%
 
2293971.0%
 
221490712.1%
 
2193< 0.1%
 
2126351.6%
 
204790.2%
 
1956< 0.1%
 

Interactions

2020-12-15T12:41:56.506686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T12:41:56.615716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T12:41:56.721260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T12:41:56.820083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T12:41:56.915603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T12:41:57.011605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T12:41:57.106508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T12:41:57.197289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T12:41:57.293185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-15T12:42:11.901869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-15T12:42:19.457289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-15T12:42:19.650251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-15T12:42:19.829604image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-15T12:42:20.021400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-15T12:42:20.203420image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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2020-12-15T12:42:12.796987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

df_indexidclickhourC1banner_possite_idsite_domainsite_categoryapp_idapp_domainapp_categorydevice_iddevice_ipdevice_modeldevice_typedevice_conn_typeC14C15C16C17C18C19C20C21
01090528.615899e+181141021001005038217daf449497bcf028772becad23867801e8d907d7df22a99f214a925ab50eac221d6c10203453002502331239-123
12655501.073791e+19014102102100515114c6723f2f38193e814130ecad23867801e8d907d7df22a99f214a7ce8e95a5ec458831019771320502227068710007548
21413001.268349e+1901410210110020ab52606371ae4aea50e219e0ecad23867801e8d907d7df22184ca3bb33b0bd88072c9f1e00169203205018990431-1117
32108944.520914e+18114102101100501fbe01fef384576728905ebdecad23867801e8d907d7df22a99f214a8eaef965d870e4de101570132050172203510008479
4636612.245924e+180141021001005085f751fdc4e18dd650e219e0685d1c4c2347f47a8ded1f7a17f6eecdc1a367a4158e4944131570432050172203510008379
5515881.718584e+19114102100100501fbe01fef384576728905ebdecad23867801e8d907d7df22a99f214ad64ec3e7a0f5f8791015704320501722035-179
63122321.447048e+190141021021005085f751fdc4e18dd650e219e085b28c93ae6375228ded1f7aa99f214ac38af552dc31133e1221611320502480329710011161
765141.090668e+19014102100100501fbe01fef384576728905ebdecad23867801e8d907d7df22a99f214a19882e8bd787e91b1015701320501722035-179
82569761.003823e+1901410210210050543a539ec7ca31083e814130ecad23867801e8d907d7df22a99f214a38fb03be3421dfe41020352320502333039-1157
92084454.222692e+18014102101100501fbe01fef384576728905ebdecad23867801e8d907d7df22a99f214a8585134c0989142d1015705320501722035-179

Last rows

df_indexidclickhourC1banner_possite_idsite_domainsite_categoryapp_idapp_domainapp_categorydevice_iddevice_ipdevice_modeldevice_typedevice_conn_typeC14C15C16C17C18C19C20C21
404182243184.735040e+18014103023100501fbe01fef384576728905ebdecad23867801e8d907d7df22a99f214aa88fedf9c6263d8a1022676320502616035-151
4041918191.712925e+18014103021100501fbe01fef384576728905ebdecad23867801e8d907d7df22a99f214aa8536f3aab145aa11022676320502616035-151
404201231122.776735e+180141030221005083a0ad1a5c9ae867f028772becad23867801e8d907d7df22a99f214a160e10c75096d134102063432050237433910011923
404212070491.788262e+19114103023100515b4d2eda16a36ef3f028772becad23867801e8d907d7df22a99f214a710f7a488a4875bd10218933205025260167-1221
404222048871.744236e+190141030231005085f751fdc4e18dd650e219e0febd113882e279960f2161f8a99f214abc14927d99e427c91021611320502480329910011161
404231531707.689337e+180141030221005085f751fdc4e18dd650e219e0e2fcccd25c5a694b0f2161f8a99f214a582ba6dcecb851b21046873205042323910014832
40424658089.965834e+181141030211005085f751fdc4e18dd650e219e09c13b4192347f47af95efa07a99f214a5d77e67dc6263d8a1023903320502740016310001317
404252203403.919389e+180141030231005085f751fdc4e18dd650e219e051cedd4eaefc06bd0f2161f8a99f214a6d7c5e3dbbeedfee1021611320502480329910011161
404262164213.143591e+180141030231005085f751fdc4e18dd650e219e0f888bf4c5b9c592b0f2161f83fe14c8905469cde3491f1bd122281832050264833910014823
40427501777.640314e+1811410302110020203b00f1c4e18dd650e219e0ecad23867801e8d907d7df220f7c61dcd1d5cc44373ecbe600234383205026842132710000452